{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f85d40cb-ecb4-4224-b6f5-144105303fb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33abcc7a-7c8c-4264-a80f-c24a5adb42a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "9ae9df89-c4c2-4dc1-8d63-cbc2ff7081b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed3d6f99-bea0-4063-a38d-8ad7cb67ade4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01927f34-3198-4358-9a1f-7f5a82d4927e",
   "metadata": {},
   "outputs": [],
   "source": [
    "now =datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf258ef2-7c58-4e66-93bf-d0da24fa5612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2024, 1, 18, 14, 14, 0, 133199)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f2f6087-787d-438d-9451-0ebc86699d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2024, 1, 18)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now.year,now.month,now.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f96744e-f09f-43f7-82b6-780ef38878ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "delta = datetime(2011,1,7) - datetime(2008,6,24,8,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b7bfa53-a52c-4ae6-b17e-0c16060cae72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=926, seconds=56700)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f3780d4-415c-49da-a9ec-7bb5affc2b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "926"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "187b82ba-e039-4b6d-b397-dbcaedb80162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56700"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta.seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3746c041-38a8-4cb8-b92e-f7c6dbc4e4a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11e11d59-e5f6-4474-a805-655da8700478",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime(2011,1,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0aea9264-c89f-4cb3-abee-2363a7c74890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 19, 0, 0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start+timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f63c74e6-280e-4701-8b2b-ee03f71792f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2010, 12, 14, 0, 0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start-2*timedelta(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "683a9163-f921-453c-8891-86389c635be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = datetime(2011,1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aaf784ee-2c6e-45e5-a6cd-c512df46f6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(stamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99feef4f-218c-48e6-b0c4-f82739783f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011-01-03 00:00:00.000000'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp.strftime('%Y-%m-%d %H:%M:%S.%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e909f1c-0773-4b29-aba7-f3bee20e4a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "value='2011-01-03'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d1ecae0-0e80-487e-b1ef-4623f86b7d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2011, 1, 3, 0, 0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime(value,'%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9bdc9f78-328a-434a-af04-82e547d4fa0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "datestrs=['7/6/2011','8/6/2011']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da441bf1-5b05-4132-b766-b571231b0a91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2011, 7, 6, 0, 0), datetime.datetime(2011, 8, 6, 0, 0)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[datetime.strptime(x,'%m/%d/%Y') for x in datestrs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "855b96c9-0d5e-4990-9a49-ee84f489ca6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "datestrs=[\"2011-07-06 12:00:00\", \"2011-08-06 00:00:00\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "091023d6-a94d-4c45-bc8c-efbb5a370669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(datestrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f25db58c-8336-4963-abdb-4bc0f0ac0175",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = pd.to_datetime(datestrs+[None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "71d2b55e-01f3-425e-8773-cc01b330c763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-07-06 12:00:00', '2011-08-06 00:00:00', 'NaT'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89d2f7a9-55bd-4c79-8dd4-acd21c8c328b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaT"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0e4b827b-592c-43e9-aca8-fd91af7a6e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8342889e-ea64-45c0-8c9b-616785e23808",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = [datetime(2011, 1, 2), datetime(2011, 1, 5),\n",
    "             datetime(2011, 1, 7), datetime(2011, 1, 8),\n",
    "           datetime(2011, 1, 10), datetime(2011, 1, 12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9dfa7fbb-6543-457b-983f-1759143fc76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.standard_normal(6),index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a3db620a-eec2-4258-aa4c-776468a8ac60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.114818\n",
       "2011-01-05   -1.655125\n",
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "2011-01-10    1.881667\n",
       "2011-01-12   -0.023409\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2278a3f-07f8-4889-ba95-c2bac96baa77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-07', '2011-01-08',\n",
       "               '2011-01-10', '2011-01-12'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2b918d0d-cda1-490e-8248-b2417409318e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.229635\n",
       "2011-01-05         NaN\n",
       "2011-01-07    2.127185\n",
       "2011-01-08         NaN\n",
       "2011-01-10    3.763335\n",
       "2011-01-12         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts+ts[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3d4ea784-1a1e-48ef-b617-d38e7b68d08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "be816f9c-bd3e-4956-a910-ca21f8bdadf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = ts.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "db9f7e6e-57ae-4471-9206-f2db27c89112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-01-02 00:00:00')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6af99291-014d-4cc7-a4f0-54d2ccf3aede",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = ts.index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4eb6afdf-534a-4cd2-8236-6eb264597a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0635922620117026"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[stamp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b02f09b0-64a4-42e4-8ea8-ca8ad5c6b3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8816673313122727"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2011-01-10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0508a319-4ba4-4949-b493-a24ae60eea2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_ts = pd.Series(np.random.standard_normal(1000),\n",
    "                   index=pd.date_range('2000-01-01',periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "34aa5570-1ca8-4e09-bb65-38f9383f10a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.428364\n",
       "2000-01-02    1.721466\n",
       "2000-01-03   -0.369967\n",
       "2000-01-04    0.570581\n",
       "2000-01-05    0.081253\n",
       "                ...   \n",
       "2002-09-22    2.811979\n",
       "2002-09-23   -0.440547\n",
       "2002-09-24    0.029056\n",
       "2002-09-25   -2.580046\n",
       "2002-09-26    0.305532\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1549fcd3-5a78-47a6-b8d7-75d6bf85f859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-01-01   -1.341540\n",
       "2001-01-02   -1.098995\n",
       "2001-01-03   -0.115274\n",
       "2001-01-04    0.608697\n",
       "2001-01-05    1.363297\n",
       "                ...   \n",
       "2001-12-27   -1.007171\n",
       "2001-12-28    0.498682\n",
       "2001-12-29    1.806691\n",
       "2001-12-30   -0.172381\n",
       "2001-12-31    0.934847\n",
       "Freq: D, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_ts['2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a26348ab-316a-4944-994b-fe6664bd69b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2001-05-01   -0.249423\n",
       "2001-05-02   -0.519023\n",
       "2001-05-03   -1.082793\n",
       "2001-05-04    0.246022\n",
       "2001-05-05   -0.697223\n",
       "2001-05-06    1.065011\n",
       "2001-05-07   -0.491620\n",
       "2001-05-08    0.356690\n",
       "2001-05-09    0.271600\n",
       "2001-05-10   -0.783083\n",
       "2001-05-11    0.516502\n",
       "2001-05-12   -0.884540\n",
       "2001-05-13    1.714859\n",
       "2001-05-14    0.278132\n",
       "2001-05-15   -1.125624\n",
       "2001-05-16    1.261123\n",
       "2001-05-17   -1.491349\n",
       "2001-05-18    0.856072\n",
       "2001-05-19   -0.633566\n",
       "2001-05-20    0.944447\n",
       "2001-05-21    0.055305\n",
       "2001-05-22   -2.368693\n",
       "2001-05-23    0.729376\n",
       "2001-05-24   -1.182355\n",
       "2001-05-25   -0.337375\n",
       "2001-05-26    0.091721\n",
       "2001-05-27   -0.739428\n",
       "2001-05-28   -1.259040\n",
       "2001-05-29   -0.173453\n",
       "2001-05-30    2.288254\n",
       "2001-05-31    0.768894\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_ts['2001-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce0b525e-4aa6-40ce-8a83-616af42248a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "2011-01-10    1.881667\n",
       "2011-01-12   -0.023409\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[datetime(2011,1,7):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "62653220-585d-410e-a309-f34a649fd277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "2011-01-10    1.881667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[datetime(2011,1,7):datetime(2011,1,10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7a218311-75dd-4ddf-a2a8-40c8e7c87fca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.114818\n",
       "2011-01-05   -1.655125\n",
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "2011-01-10    1.881667\n",
       "2011-01-12   -0.023409\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7cf3cec8-4849-44a7-a7fa-72e3f4a97651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "2011-01-10    1.881667\n",
       "dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts['2011-01-06':'2011-01-11']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e8da4882-27dd-47ae-a2af-2b1c6d50a275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.114818\n",
       "2011-01-05   -1.655125\n",
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.truncate(after='2011-01-09')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "baaa4a31-a235-45f4-a334-913dcfaddbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('2000-01-01',periods=100,freq='W-WED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a6d00296-3728-4072-9a34-fceed3ddabd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "long_df=pd.DataFrame(np.random.standard_normal((100,4)),\n",
    "                    index=dates,\n",
    "                    columns=[\"Colorado\", \"Texas\",\"New York\", \"Ohio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "482bc964-6f49-4a94-97d3-8986dc28ca12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-05-02</th>\n",
       "      <td>-0.869600</td>\n",
       "      <td>0.408231</td>\n",
       "      <td>-0.866168</td>\n",
       "      <td>-1.435123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-09</th>\n",
       "      <td>1.143244</td>\n",
       "      <td>-0.059389</td>\n",
       "      <td>1.130799</td>\n",
       "      <td>0.252838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-16</th>\n",
       "      <td>-0.388505</td>\n",
       "      <td>1.181892</td>\n",
       "      <td>-0.099179</td>\n",
       "      <td>-1.762029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-23</th>\n",
       "      <td>0.871848</td>\n",
       "      <td>-0.468549</td>\n",
       "      <td>0.876441</td>\n",
       "      <td>-0.271454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-05-30</th>\n",
       "      <td>0.010948</td>\n",
       "      <td>-0.875283</td>\n",
       "      <td>-1.822058</td>\n",
       "      <td>-0.011488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2001-05-02 -0.869600  0.408231 -0.866168 -1.435123\n",
       "2001-05-09  1.143244 -0.059389  1.130799  0.252838\n",
       "2001-05-16 -0.388505  1.181892 -0.099179 -1.762029\n",
       "2001-05-23  0.871848 -0.468549  0.876441 -0.271454\n",
       "2001-05-30  0.010948 -0.875283 -1.822058 -0.011488"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_df.loc['2001-05']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "12906f67-2895-4408-847c-64f8b7436587",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.DatetimeIndex([\"2000-01-01\", \"2000-01-02\", \"2000-01-02\",\n",
    "                          \"2000-01-02\", \"2000-01-03\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "75905aee-8608-489f-8fb6-f929f04783aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "dup_ts = pd.Series(np.arange(5),index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "70813a44-f2f8-4021-a112-92fcc383bdfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0\n",
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "2000-01-03    4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cbffb742-a97b-4179-b04e-51897e9669ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8fae9923-9ef8-4827-81f6-be191b2c7cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts['2000-01-03']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3041dafe-bbc3-4cb6-b8fa-a9c5a7af83a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-02    1\n",
       "2000-01-02    2\n",
       "2000-01-02    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dup_ts['2000-01-02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "272d1af3-0e56-4314-aae7-42eb7f54ee7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped=dup_ts.groupby(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2410e796-6ccb-48cd-b015-dce2c445e9ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    0.0\n",
       "2000-01-02    2.0\n",
       "2000-01-03    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fca1a778-cfc0-4916-bdb6-d4091820ced1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    1\n",
       "2000-01-02    3\n",
       "2000-01-03    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0e692b33-6bbf-4e15-9460-a4268a297eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-01-02    0.114818\n",
       "2011-01-05   -1.655125\n",
       "2011-01-07    1.063592\n",
       "2011-01-08    0.897651\n",
       "2011-01-10    1.881667\n",
       "2011-01-12   -0.023409\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "76777077-3d27-4ea3-ab65-3de2d6a8bfde",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampler = ts.resample('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b34fe1c8-1ec9-49db-bf1d-98373f60330e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x00000168CF9E7CA0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "52b456ed-dd4a-4a75-8597-ff4053d76d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.date_range('2012-04-01','2012-06-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e5cf66bc-933e-4678-86c7-14d7dafc97be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20',\n",
       "               '2012-04-21', '2012-04-22', '2012-04-23', '2012-04-24',\n",
       "               '2012-04-25', '2012-04-26', '2012-04-27', '2012-04-28',\n",
       "               '2012-04-29', '2012-04-30', '2012-05-01', '2012-05-02',\n",
       "               '2012-05-03', '2012-05-04', '2012-05-05', '2012-05-06',\n",
       "               '2012-05-07', '2012-05-08', '2012-05-09', '2012-05-10',\n",
       "               '2012-05-11', '2012-05-12', '2012-05-13', '2012-05-14',\n",
       "               '2012-05-15', '2012-05-16', '2012-05-17', '2012-05-18',\n",
       "               '2012-05-19', '2012-05-20', '2012-05-21', '2012-05-22',\n",
       "               '2012-05-23', '2012-05-24', '2012-05-25', '2012-05-26',\n",
       "               '2012-05-27', '2012-05-28', '2012-05-29', '2012-05-30',\n",
       "               '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6d10fba3-0a12-4222-9fd6-7b174f262ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-04-01', '2012-04-02', '2012-04-03', '2012-04-04',\n",
       "               '2012-04-05', '2012-04-06', '2012-04-07', '2012-04-08',\n",
       "               '2012-04-09', '2012-04-10', '2012-04-11', '2012-04-12',\n",
       "               '2012-04-13', '2012-04-14', '2012-04-15', '2012-04-16',\n",
       "               '2012-04-17', '2012-04-18', '2012-04-19', '2012-04-20'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2012-04-01',periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8e561e18-52ad-4a17-bac4-8238760df81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-13', '2012-05-14', '2012-05-15', '2012-05-16',\n",
       "               '2012-05-17', '2012-05-18', '2012-05-19', '2012-05-20',\n",
       "               '2012-05-21', '2012-05-22', '2012-05-23', '2012-05-24',\n",
       "               '2012-05-25', '2012-05-26', '2012-05-27', '2012-05-28',\n",
       "               '2012-05-29', '2012-05-30', '2012-05-31', '2012-06-01'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(end='2012-06-01',periods=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c425a30f-cfa7-4790-81b9-7f4a5b56f21c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-31', '2000-02-29', '2000-03-31', '2000-04-28',\n",
       "               '2000-05-31', '2000-06-30', '2000-07-31', '2000-08-31',\n",
       "               '2000-09-29', '2000-10-31', '2000-11-30'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01','2000-12-01',freq='BM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6f804345-4873-4654-9d8f-8e0088f9efc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02 12:56:31', '2012-05-03 12:56:31',\n",
       "               '2012-05-04 12:56:31', '2012-05-05 12:56:31',\n",
       "               '2012-05-06 12:56:31'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-05-02 12:56:31',periods=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5920043f-1db6-4988-b8f6-67395c3dd57f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-05-02', '2012-05-03', '2012-05-04', '2012-05-05',\n",
       "               '2012-05-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-05-02 12:56:31',periods=5,normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "76570ac4-cad5-4e41-94de-6f3655665153",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import Hour,Minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "87714136-c669-4755-a6b5-3e7e75b7906f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hour = Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ff6477d6-84c6-4f02-af0c-aa71dfb37a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Hour>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ef18c841-e1c2-4ada-92d6-69c39c1a0e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "four_hours=Hour(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3d3e869e-2d3f-4b1f-aeda-d035db1601ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4 * Hours>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "four_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b43c2ec3-d691-4e6e-bf70-6cd33ba171da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 04:00:00',\n",
       "               '2000-01-01 08:00:00', '2000-01-01 12:00:00',\n",
       "               '2000-01-01 16:00:00', '2000-01-01 20:00:00',\n",
       "               '2000-01-02 00:00:00', '2000-01-02 04:00:00',\n",
       "               '2000-01-02 08:00:00', '2000-01-02 12:00:00',\n",
       "               '2000-01-02 16:00:00', '2000-01-02 20:00:00',\n",
       "               '2000-01-03 00:00:00', '2000-01-03 04:00:00',\n",
       "               '2000-01-03 08:00:00', '2000-01-03 12:00:00',\n",
       "               '2000-01-03 16:00:00', '2000-01-03 20:00:00'],\n",
       "              dtype='datetime64[ns]', freq='4H')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01','2000-01-03 23:59',freq='4H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ca24221c-4816-4b92-b026-aba690e2d334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<150 * Minutes>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Hour(2)+Minute(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7f1137a1-75b6-4495-be3a-18653196f5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:30:00',\n",
       "               '2000-01-01 03:00:00', '2000-01-01 04:30:00',\n",
       "               '2000-01-01 06:00:00', '2000-01-01 07:30:00',\n",
       "               '2000-01-01 09:00:00', '2000-01-01 10:30:00',\n",
       "               '2000-01-01 12:00:00', '2000-01-01 13:30:00'],\n",
       "              dtype='datetime64[ns]', freq='90T')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2000-01-01',periods=10,freq='1h30min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "391f013e-63f0-4cd3-8467-7cfa407069b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_dates = pd.date_range(\"2012-01-01\", \"2012-09-01\",freq='WOM-3FRI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3dd44523-03c5-4848-a8f6-f495a8928082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2012-01-20 00:00:00'),\n",
       " Timestamp('2012-02-17 00:00:00'),\n",
       " Timestamp('2012-03-16 00:00:00'),\n",
       " Timestamp('2012-04-20 00:00:00'),\n",
       " Timestamp('2012-05-18 00:00:00'),\n",
       " Timestamp('2012-06-15 00:00:00'),\n",
       " Timestamp('2012-07-20 00:00:00'),\n",
       " Timestamp('2012-08-17 00:00:00')]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(monthly_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "65af95a3-2006-4a99-819e-aca86e24d257",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts =pd.Series(np.random.standard_normal(4),\n",
    "              index=pd.date_range(\"2000-01-01\", periods=4, freq=\"M\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b801baf0-43dc-40a3-b8e0-85ed9751eb12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.073740\n",
       "2000-02-29    0.276218\n",
       "2000-03-31   -0.172929\n",
       "2000-04-30    1.107103\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "74f3ca4a-5a36-4d7b-8169-8b14f0db1cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29         NaN\n",
       "2000-03-31   -0.073740\n",
       "2000-04-30    0.276218\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8a825cc5-77d0-4132-8b4b-b0fdd0df5c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -0.172929\n",
       "2000-02-29    1.107103\n",
       "2000-03-31         NaN\n",
       "2000-04-30         NaN\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "631d6b64-8c46-455b-81c7-af58b641c88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31         NaN\n",
       "2000-02-29   -4.745866\n",
       "2000-03-31   -1.626058\n",
       "2000-04-30   -7.402077\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts/ts.shift(1)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "c0cc2ffb-82da-406c-ad26-ee888f483a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-03-31   -0.073740\n",
       "2000-04-30    0.276218\n",
       "2000-05-31   -0.172929\n",
       "2000-06-30    1.107103\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(2,freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "70bcf6fe-de9b-42b4-8583-148928d2f13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-02-03   -0.073740\n",
       "2000-03-03    0.276218\n",
       "2000-04-03   -0.172929\n",
       "2000-05-03    1.107103\n",
       "dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(3,freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9bc70057-2c34-43ed-8ace-17ae1f98c438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31 01:30:00   -0.073740\n",
       "2000-02-29 01:30:00    0.276218\n",
       "2000-03-31 01:30:00   -0.172929\n",
       "2000-04-30 01:30:00    1.107103\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.shift(1,freq='90T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7617c68a-97bb-46c4-9d99-1185d1d0f706",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import Day,MonthEnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "f16cfe93-2493-4b18-8911-060b95b59465",
   "metadata": {},
   "outputs": [],
   "source": [
    "now=datetime(2011,11,17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f0c59a05-b84c-4bfd-acf7-c0a4970d190f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-20 00:00:00')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now+3*Day()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ef296ea5-420c-4a7b-8c11-8bf8a774abe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now+MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "af1e1601-98de-44f4-84d5-ccec96ec82cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-12-31 00:00:00')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now+MonthEnd(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "75f71b1d-648e-4d93-ac91-83cc04050960",
   "metadata": {},
   "outputs": [],
   "source": [
    "offset =MonthEnd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c09ff038-20ff-47d7-b7af-490885863ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-11-30 00:00:00')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollforward(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "86b9f214-f197-4b88-9b2a-ab04e7abbeac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-10-31 00:00:00')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "offset.rollback(now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "af10d7ee-1b0a-4f87-84ec-877abc38d12f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.standard_normal(20),\n",
    "               index=pd.date_range('2000-01-15',periods=20,freq='4D'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9deb0618-e200-4d19-952d-7aeb74acb641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-15   -0.130664\n",
       "2000-01-19    0.509882\n",
       "2000-01-23    1.123618\n",
       "2000-01-27   -0.582290\n",
       "2000-01-31    0.066299\n",
       "2000-02-04   -0.016157\n",
       "2000-02-08   -2.396638\n",
       "2000-02-12   -0.330093\n",
       "2000-02-16    0.826283\n",
       "2000-02-20    1.556469\n",
       "2000-02-24    0.627116\n",
       "2000-02-28    2.007897\n",
       "2000-03-03   -0.276745\n",
       "2000-03-07   -0.613719\n",
       "2000-03-11    0.769230\n",
       "2000-03-15    0.724876\n",
       "2000-03-19    0.823015\n",
       "2000-03-23   -0.529623\n",
       "2000-03-27   -1.040132\n",
       "2000-03-31    0.223015\n",
       "Freq: 4D, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "46bf71f1-4a10-444c-94be-4a710ab93563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.197369\n",
       "2000-02-29    0.324983\n",
       "2000-03-31    0.009990\n",
       "dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.groupby(MonthEnd().rollforward).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "2dd8b2da-d31a-46fd-9b90-fa1f9edfe9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.197369\n",
       "2000-02-29    0.324983\n",
       "2000-03-31    0.009990\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bb604653-9753-4e71-a70a-9af3e9aedded",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "7b6f8393-d289-483f-a0bf-a0b18613cac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific', 'UTC']"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pytz.common_timezones[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "dc533e7c-6556-485e-ace1-4f2f4142eada",
   "metadata": {},
   "outputs": [],
   "source": [
    "tz = pytz.timezone('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "92075ef5-23b3-4e5f-9a1a-90e137243c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DstTzInfo 'America/New_York' LMT-1 day, 19:04:00 STD>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c23ebdef-69e3-4a17-96e8-94b1f2470b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('2012-03-09 09:30',periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "0ad2a0ae-58c9-4cd4-a568-23a18fa9c7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.standard_normal(len(dates)),index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "193556ad-ce56-4eb3-a2d2-bf82cecb78d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.885164\n",
       "2012-03-10 09:30:00   -1.031106\n",
       "2012-03-11 09:30:00   -0.230677\n",
       "2012-03-12 09:30:00    1.470922\n",
       "2012-03-13 09:30:00    0.279807\n",
       "2012-03-14 09:30:00   -0.505243\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1fc421fe-480d-4541-a377-d864e3acddaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(ts.index.tz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "24565cb7-ea22-4593-a3a4-d5cbf4e7788e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00', '2012-03-16 09:30:00+00:00',\n",
       "               '2012-03-17 09:30:00+00:00', '2012-03-18 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2012-03-09 09:30',periods=10,tz='UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ccc2a05e-2999-4411-86dc-fce3f32f41c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00   -0.885164\n",
       "2012-03-10 09:30:00   -1.031106\n",
       "2012-03-11 09:30:00   -0.230677\n",
       "2012-03-12 09:30:00    1.470922\n",
       "2012-03-13 09:30:00    0.279807\n",
       "2012-03-14 09:30:00   -0.505243\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "5b660331-f6ee-4774-a121-e6d51f904881",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_utc = ts.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "7e3cf525-a9c1-4221-b486-ffdf66da7835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 09:30:00+00:00   -0.885164\n",
       "2012-03-10 09:30:00+00:00   -1.031106\n",
       "2012-03-11 09:30:00+00:00   -0.230677\n",
       "2012-03-12 09:30:00+00:00    1.470922\n",
       "2012-03-13 09:30:00+00:00    0.279807\n",
       "2012-03-14 09:30:00+00:00   -0.505243\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "00571c75-ff55-45dd-9731-ffcade2efa25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+00:00', '2012-03-10 09:30:00+00:00',\n",
       "               '2012-03-11 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq='D')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "8fb359a0-c5ef-4438-b892-74d7ee02f2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 04:30:00-05:00   -0.885164\n",
       "2012-03-10 04:30:00-05:00   -1.031106\n",
       "2012-03-11 05:30:00-04:00   -0.230677\n",
       "2012-03-12 05:30:00-04:00    1.470922\n",
       "2012-03-13 05:30:00-04:00    0.279807\n",
       "2012-03-14 05:30:00-04:00   -0.505243\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "2c4254d3-c49d-4c7a-a502-56be6028c2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_eastern = ts.tz_localize(\"America/New_York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "d8422f8b-df2c-4e38-a355-974671266ab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 14:30:00+00:00   -0.885164\n",
       "2012-03-10 14:30:00+00:00   -1.031106\n",
       "2012-03-11 13:30:00+00:00   -0.230677\n",
       "2012-03-12 13:30:00+00:00    1.470922\n",
       "2012-03-13 13:30:00+00:00    0.279807\n",
       "2012-03-14 13:30:00+00:00   -0.505243\n",
       "dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0280cb15-7c66-4026-8de3-b151437a8ffb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-09 15:30:00+01:00   -0.885164\n",
       "2012-03-10 15:30:00+01:00   -1.031106\n",
       "2012-03-11 14:30:00+01:00   -0.230677\n",
       "2012-03-12 14:30:00+01:00    1.470922\n",
       "2012-03-13 14:30:00+01:00    0.279807\n",
       "2012-03-14 14:30:00+01:00   -0.505243\n",
       "dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_eastern.tz_convert(\"Europe/Berlin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "544cc3e0-a218-450d-b402-5f4e2fbc744d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-09 09:30:00+08:00', '2012-03-10 09:30:00+08:00',\n",
       "               '2012-03-11 09:30:00+08:00', '2012-03-12 09:30:00+08:00',\n",
       "               '2012-03-13 09:30:00+08:00', '2012-03-14 09:30:00+08:00'],\n",
       "              dtype='datetime64[ns, Asia/Shanghai]', freq=None)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index.tz_localize(\"Asia/Shanghai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a85b5199-c4b7-4bdf-b01f-05f05cfd8f19",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = pd.Timestamp('2011-03-12 04:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3fe7af4f-7052-4540-a73a-d9574fbcd5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp_utc = stamp.tz_localize('utc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8cbd68fa-c7c0-4bbd-922f-4d28056502ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-11 23:00:00-0500', tz='America/New_York')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('America/New_York')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "ff3fc7d8-956d-48d4-89c7-38c738e87486",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp_moscow =pd.Timestamp(\"2011-03-12 04:00\", tz=\"Europe/Moscow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7e7a256a-c471-4a82-bbdb-582f276e31bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-03-12 04:00:00+0300', tz='Europe/Moscow')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_moscow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "1589d6ab-ac65-45fa-b80e-a4158bead42f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d429ceca-b1bc-4573-a96b-e157dd311500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1299902400000000000"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp_utc.tz_convert('America/New_York').value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "04765721-7ad1-492e-b0e8-63172aeb88e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = pd.Timestamp(\"2012-03-11 01:30\", tz=\"US/Eastern\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "61e3501a-0373-453c-99d2-a17b90962c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-11 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "8bdba5b5-6bec-4152-8e5f-fa568c4dfa14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-03-11 03:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp + Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "39329e86-2704-47cd-a349-b4fca9545743",
   "metadata": {},
   "outputs": [],
   "source": [
    "stamp = pd.Timestamp(\"2012-11-04 00:30\", tz=\"US/Eastern\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "5de04df7-7046-4b07-bdf4-1a819b9594dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 00:30:00-0400', tz='US/Eastern')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "3c5f9c5f-86c9-420c-8f0f-744c7f1f43d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-11-04 01:30:00-0500', tz='US/Eastern')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stamp + 2 * Hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c2b0ba50-7d35-4650-aa99-7faf270812aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range(\"2012-03-07 09:30\", periods=10, freq=\"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c434d0d4-0521-4377-906d-488943548faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.standard_normal(len(dates)), index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "fec481c6-5bf4-4a5d-86df-9e1cee76997d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-07 09:30:00    1.503898\n",
       "2012-03-08 09:30:00   -1.035136\n",
       "2012-03-09 09:30:00    0.034981\n",
       "2012-03-12 09:30:00   -0.656038\n",
       "2012-03-13 09:30:00    0.524339\n",
       "2012-03-14 09:30:00   -2.723537\n",
       "2012-03-15 09:30:00   -0.239827\n",
       "2012-03-16 09:30:00   -0.781859\n",
       "2012-03-19 09:30:00    0.300703\n",
       "2012-03-20 09:30:00   -1.349589\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "16e056d8-7ca2-4e4d-bca2-51ccb93c4b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts1 = ts[:7].tz_localize(\"Europe/London\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3459c97b-dcb5-41f3-8f31-33049b63f971",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts2 = ts1[2:].tz_convert(\"Europe/Moscow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "9f92c393-f130-441d-af91-54b1493f8e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result =ts1+ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "c9ca496c-5a92-4721-bbf5-2768b719d789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-07 09:30:00+00:00', '2012-03-08 09:30:00+00:00',\n",
       "               '2012-03-09 09:30:00+00:00', '2012-03-12 09:30:00+00:00',\n",
       "               '2012-03-13 09:30:00+00:00', '2012-03-14 09:30:00+00:00',\n",
       "               '2012-03-15 09:30:00+00:00'],\n",
       "              dtype='datetime64[ns, UTC]', freq=None)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "8346ba19-b5fc-4a72-8fb5-4bad5b3710f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('2011',freq='A-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "bc30746e-12bf-4a44-a01c-ef9b7b66ffa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011', 'A-DEC')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d6ac13d2-7e6a-4831-a7e1-ac74c1bb4392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2016', 'A-DEC')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "fa6796a7-4260-4aee-975f-c000801c1a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2009', 'A-DEC')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "52828aed-0dbd-4e34-b31f-c88a114205e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3 * YearEnds: month=12>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Period('2014',freq='A-DEC') -p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "ed216876-7f31-4e96-8889-bd0171fa31e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = pd.period_range(\"2000-01-01\", \"2000-06-30\",freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "c8c4298b-77e4-4963-8998-e3cb3a4830c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06'], dtype='period[M]')"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "7a09bf9e-dc38-4b58-860e-d42d9535e357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.993135\n",
       "2000-02   -0.556718\n",
       "2000-03    0.118911\n",
       "2000-04   -1.314103\n",
       "2000-05   -1.486033\n",
       "2000-06    1.742395\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.random.standard_normal(6), index=periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "74ed6de4-f315-4473-b251-4fc39640d4dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = [\"2001Q3\", \"2002Q2\", \"2003Q1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "e891b300-d410-4365-b186-43c3997a45d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.PeriodIndex(values, freq=\"Q-DEC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "31944a2a-b7a1-418f-a342-6f7d245d37db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2001Q3', '2002Q2', '2003Q1'], dtype='period[Q-DEC]')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "433b3608-bfe1-441b-be0c-5734478f0116",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period('2011',freq='A-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "96a9e612-8129-479c-bbd0-5856965215ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011', 'A-DEC')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "45e9c7a8-9a7d-4025-8a6c-83397da12313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-01', 'M')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "ab58e332-6555-4d51-8ad3-97715ea69173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-12', 'M')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M',how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "acb16fea-732b-4ab2-b47d-1451d2a9e3f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-12', 'M')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "ab1879c6-71ff-42bb-a2bb-a99ddd791144",
   "metadata": {},
   "outputs": [],
   "source": [
    "p=pd.Period('2011',freq='A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "47656ec1-aa57-4018-83a1-54a18dea2acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011', 'A-JUN')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "3d7ab2d0-361d-46f3-8dff-82b4c6687a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2010-07', 'M')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "80299bd6-d430-4519-8941-b2d32a93f207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-06', 'M')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('M',how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "5464459a-1ff9-4c95-82e8-255f6f8aa13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period(\"Aug-2011\", \"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "83f2e5f6-e219-4fbe-b6c2-93fb09fb0fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012', 'A-JUN')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('A-JUN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "ccfd9ec4-aa59-429e-9839-c09cb6491160",
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = pd.period_range(\"2006\", \"2009\", freq=\"A-DEC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "69ef568e-46d5-4e71-b274-4557df8c6949",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.standard_normal(len(periods)), index=periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "13686211-6325-421a-8999-ea95d846e0ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006    0.505967\n",
       "2007   -0.733863\n",
       "2008    0.540314\n",
       "2009    0.929829\n",
       "Freq: A-DEC, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "0960b299-1ed9-4c11-b649-659166130010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2006-01    0.505967\n",
       "2007-01   -0.733863\n",
       "2008-01    0.540314\n",
       "2009-01    0.929829\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq(\"M\", how=\"start\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "e847ef66-7635-463c-849e-2df2a08733cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_23588\\2984082239.py:1: FutureWarning: PeriodDtype[B] is deprecated and will be removed in a future version. Use a DatetimeIndex with freq='B' instead\n",
      "  ts.asfreq(\"B\", how=\"end\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2006-12-29    0.505967\n",
       "2007-12-31   -0.733863\n",
       "2008-12-31    0.540314\n",
       "2009-12-31    0.929829\n",
       "Freq: B, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.asfreq(\"B\", how=\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "33389848-1171-4967-8b97-a446c02cb8e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = pd.Period(\"2012Q4\", freq=\"Q-JAN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c8f85c2a-4a89-4f25-87c7-4c8739a33532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012Q4', 'Q-JAN')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "eb3a9a24-552e-47ec-b691-edb7b911e8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2011-11-01', 'D')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D',how='start')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "9bc3f6a9-1c94-4c60-b6a4-a7f018a9f63e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-31', 'D')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.asfreq('D',how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "90033b4d-4fe1-4ed6-8c55-213639bbcdd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_23588\\2240886740.py:1: FutureWarning: Period with BDay freq is deprecated and will be removed in a future version. Use a DatetimeIndex with BDay freq instead.\n",
      "  p4pm = (p.asfreq(\"B\", how=\"end\") - 1).asfreq(\"T\", how=\"start\") + 16 * 6\n"
     ]
    }
   ],
   "source": [
    "p4pm = (p.asfreq(\"B\", how=\"end\") - 1).asfreq(\"T\", how=\"start\") + 16 * 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "84ed957a-1eb4-478b-abe9-829ce5a1cbee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period('2012-01-30 01:36', 'T')"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "cb7703b2-12fe-4673-860b-238b2c174497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2012-01-30 01:36:00')"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p4pm.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "d8e01d3b-7d16-4983-aeb4-4497b7c05a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "periods = pd.period_range(\"2011Q3\", \"2012Q4\", freq=\"Q-JAN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "04462c8b-3979-403d-9084-68551aa4af41",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.arange(len(periods)),index=periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "a5ffc533-b324-45f1-a352-0b5544e46f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011Q3    0\n",
       "2011Q4    1\n",
       "2012Q1    2\n",
       "2012Q2    3\n",
       "2012Q3    4\n",
       "2012Q4    5\n",
       "Freq: Q-JAN, dtype: int32"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "842385bf-cc05-47d1-ac1f-743bf2689d92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BF\\AppData\\Local\\Temp\\ipykernel_23588\\12396575.py:1: FutureWarning: PeriodDtype[B] is deprecated and will be removed in a future version. Use a DatetimeIndex with freq='B' instead\n",
      "  new_periods = (periods.asfreq('B','end')-1).asfreq('H','start')+1\n"
     ]
    }
   ],
   "source": [
    "new_periods = (periods.asfreq('B','end')-1).asfreq('H','start')+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "1e4007ca-70a3-4b16-9ac0-bd1e9232a4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = new_periods.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "117a4268-a04d-46e0-ab1d-e07d4ad3b2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010-10-28 01:00:00    0\n",
       "2011-01-28 01:00:00    1\n",
       "2011-04-28 01:00:00    2\n",
       "2011-07-28 01:00:00    3\n",
       "2011-10-28 01:00:00    4\n",
       "2012-01-30 01:00:00    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "90b4c3cf-83db-4a75-affe-2da2d739acf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('2000-01-01',periods=3,freq='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "e97317c6-0de0-452a-be09-507afdb3ad37",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.standard_normal(3),index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "e9a06111-20d1-484e-8204-775c71887443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31   -1.589357\n",
       "2000-02-29    0.713541\n",
       "2000-03-31   -0.231275\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "5a15152e-20c4-4739-8546-633f4e615dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pts = ts.to_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "e65064ce-1a73-4a9d-a6c8-ddb80f808315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -1.589357\n",
       "2000-02    0.713541\n",
       "2000-03   -0.231275\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "e066b419-f954-4667-93f8-9df1b78392a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range(\"2000-01-29\", periods=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "07973d35-2515-4dab-8445-623c6011f673",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts2 = pd.Series(np.random.standard_normal(6), index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "da9d7381-c0dd-46be-b04b-817d958face5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29   -0.846183\n",
       "2000-01-30    0.471166\n",
       "2000-01-31   -0.997425\n",
       "2000-02-01   -3.201791\n",
       "2000-02-02   -0.572870\n",
       "2000-02-03    1.307412\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "450a5431-57a7-4ff5-ac1e-a980e2dfaf00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01   -0.846183\n",
       "2000-01    0.471166\n",
       "2000-01   -0.997425\n",
       "2000-02   -3.201791\n",
       "2000-02   -0.572870\n",
       "2000-02    1.307412\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts2.to_period(\"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "f8b0c8c0-ed37-4d26-842e-cf42b3a9ea59",
   "metadata": {},
   "outputs": [],
   "source": [
    "pts = ts2.to_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "3c3362a1-0609-4385-bbe6-4230e30ee26d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29   -0.846183\n",
       "2000-01-30    0.471166\n",
       "2000-01-31   -0.997425\n",
       "2000-02-01   -3.201791\n",
       "2000-02-02   -0.572870\n",
       "2000-02-03    1.307412\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "3923f8f7-5964-49a9-9c3e-3ba088807307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-29 23:59:59.999999999   -0.846183\n",
       "2000-01-30 23:59:59.999999999    0.471166\n",
       "2000-01-31 23:59:59.999999999   -0.997425\n",
       "2000-02-01 23:59:59.999999999   -3.201791\n",
       "2000-02-02 23:59:59.999999999   -0.572870\n",
       "2000-02-03 23:59:59.999999999    1.307412\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts.to_timestamp(how='end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "4bd02377-02dd-492e-adab-4cc24cb413e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('examples/macrodata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "881517d7-33c0-44a5-ad70-e184d5136bff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>quarter</th>\n",
       "      <th>realgdp</th>\n",
       "      <th>realcons</th>\n",
       "      <th>realinv</th>\n",
       "      <th>realgovt</th>\n",
       "      <th>realdpi</th>\n",
       "      <th>cpi</th>\n",
       "      <th>m1</th>\n",
       "      <th>tbilrate</th>\n",
       "      <th>unemp</th>\n",
       "      <th>pop</th>\n",
       "      <th>infl</th>\n",
       "      <th>realint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>2710.349</td>\n",
       "      <td>1707.4</td>\n",
       "      <td>286.898</td>\n",
       "      <td>470.045</td>\n",
       "      <td>1886.9</td>\n",
       "      <td>28.98</td>\n",
       "      <td>139.7</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.8</td>\n",
       "      <td>177.146</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>2778.801</td>\n",
       "      <td>1733.7</td>\n",
       "      <td>310.859</td>\n",
       "      <td>481.301</td>\n",
       "      <td>1919.7</td>\n",
       "      <td>29.15</td>\n",
       "      <td>141.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>5.1</td>\n",
       "      <td>177.830</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>2775.488</td>\n",
       "      <td>1751.8</td>\n",
       "      <td>289.226</td>\n",
       "      <td>491.260</td>\n",
       "      <td>1916.4</td>\n",
       "      <td>29.35</td>\n",
       "      <td>140.5</td>\n",
       "      <td>3.82</td>\n",
       "      <td>5.3</td>\n",
       "      <td>178.657</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>2785.204</td>\n",
       "      <td>1753.7</td>\n",
       "      <td>299.356</td>\n",
       "      <td>484.052</td>\n",
       "      <td>1931.3</td>\n",
       "      <td>29.37</td>\n",
       "      <td>140.0</td>\n",
       "      <td>4.33</td>\n",
       "      <td>5.6</td>\n",
       "      <td>179.386</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>2847.699</td>\n",
       "      <td>1770.5</td>\n",
       "      <td>331.722</td>\n",
       "      <td>462.199</td>\n",
       "      <td>1955.5</td>\n",
       "      <td>29.54</td>\n",
       "      <td>139.6</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>180.007</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
       "0  1959        1  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
       "1  1959        2  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
       "2  1959        3  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
       "3  1959        4  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
       "4  1960        1  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
       "\n",
       "      m1  tbilrate  unemp      pop  infl  realint  \n",
       "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
       "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
       "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
       "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
       "4  139.6      3.50    5.2  180.007  2.31     1.19  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "e347edc2-0f85-416e-ab1e-186403f8230b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1959\n",
       "1      1959\n",
       "2      1959\n",
       "3      1959\n",
       "4      1960\n",
       "       ... \n",
       "198    2008\n",
       "199    2008\n",
       "200    2009\n",
       "201    2009\n",
       "202    2009\n",
       "Name: year, Length: 203, dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "b61d1dad-8771-4bd3-b602-f83c35611624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      2\n",
       "2      3\n",
       "3      4\n",
       "4      1\n",
       "      ..\n",
       "198    3\n",
       "199    4\n",
       "200    1\n",
       "201    2\n",
       "202    3\n",
       "Name: quarter, Length: 203, dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['quarter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "0befc19f-1bd7-4681-ad16-f59fd6a00da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.PeriodIndex(year=data['year'],quarter=data['quarter'],freq='Q-DEC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "ec9af484-4299-472a-91dc-6d4b3544b86a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1959Q1', '1959Q2', '1959Q3', '1959Q4', '1960Q1', '1960Q2',\n",
       "             '1960Q3', '1960Q4', '1961Q1', '1961Q2',\n",
       "             ...\n",
       "             '2007Q2', '2007Q3', '2007Q4', '2008Q1', '2008Q2', '2008Q3',\n",
       "             '2008Q4', '2009Q1', '2009Q2', '2009Q3'],\n",
       "            dtype='period[Q-DEC]', length=203)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "99df2236-36c4-4867-9466-ec36b833dd5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.index = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "137606c0-c8fc-415f-898b-7aad08f8653f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1959Q1    0.00\n",
       "1959Q2    2.34\n",
       "1959Q3    2.74\n",
       "1959Q4    0.27\n",
       "1960Q1    2.31\n",
       "          ... \n",
       "2008Q3   -3.16\n",
       "2008Q4   -8.79\n",
       "2009Q1    0.94\n",
       "2009Q2    3.37\n",
       "2009Q3    3.56\n",
       "Freq: Q-DEC, Name: infl, Length: 203, dtype: float64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['infl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "9725c297-8215-4d23-983e-a592adc1abfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range(\"2000-01-01\", periods=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "793da151-5046-4baa-a227-7e1deaa93b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts=pd.Series(np.random.standard_normal(len(dates)),index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "6988ea73-227c-44b5-9044-854425ea63be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01   -0.489945\n",
       "2000-01-02   -0.493701\n",
       "2000-01-03    2.932655\n",
       "2000-01-04    0.366101\n",
       "2000-01-05    0.631200\n",
       "                ...   \n",
       "2000-04-05   -0.052547\n",
       "2000-04-06    0.655620\n",
       "2000-04-07    0.561409\n",
       "2000-04-08   -1.883982\n",
       "2000-04-09   -0.713537\n",
       "Freq: D, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c758ca61-5229-4567-bd40-c77cfbd2b4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-31    0.139151\n",
       "2000-02-29   -0.036994\n",
       "2000-03-31   -0.009493\n",
       "2000-04-30    0.050362\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "8b39792a-83e2-41c4-bd47-5eae1c482ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01    0.139151\n",
       "2000-02   -0.036994\n",
       "2000-03   -0.009493\n",
       "2000-04    0.050362\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('M',kind='period').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "7275fe60-4215-4992-856b-e1cc6c874073",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range('2000-01-01',periods=12,freq='T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "4283fd39-ea03-484b-95ad-36199b48b791",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.arange(len(dates)),index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "864c0b68-e1cd-4caa-9880-14712c151317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:01:00     1\n",
       "2000-01-01 00:02:00     2\n",
       "2000-01-01 00:03:00     3\n",
       "2000-01-01 00:04:00     4\n",
       "2000-01-01 00:05:00     5\n",
       "2000-01-01 00:06:00     6\n",
       "2000-01-01 00:07:00     7\n",
       "2000-01-01 00:08:00     8\n",
       "2000-01-01 00:09:00     9\n",
       "2000-01-01 00:10:00    10\n",
       "2000-01-01 00:11:00    11\n",
       "Freq: T, dtype: int32"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "69931631-8bc8-4267-8ba5-bbc9ddd6a8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00    10\n",
       "2000-01-01 00:05:00    35\n",
       "2000-01-01 00:10:00    21\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "15f797a8-6673-474f-9dd3-fab57cee6adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:55:00     0\n",
       "2000-01-01 00:00:00    15\n",
       "2000-01-01 00:05:00    40\n",
       "2000-01-01 00:10:00    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "7ff8cbe1-ac76-4585-8719-a8038ae65075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:05:00    15\n",
       "2000-01-01 00:10:00    40\n",
       "2000-01-01 00:15:00    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min',closed='right',label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "e51a80bd-f904-438f-b317-2b48be765745",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.frequencies import to_offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "bccd4e16-93d1-48cd-9297-5e4933ef5acb",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ts.resample('5min',closed='right',label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "ad0c0181-fe45-4c6c-a40d-b0b873f0aeef",
   "metadata": {},
   "outputs": [],
   "source": [
    "result.index = result.index + to_offset(\"-1s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "b4a455cf-8095-4865-9ede-43df4ee7c5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999-12-31 23:59:59     0\n",
       "2000-01-01 00:04:59    15\n",
       "2000-01-01 00:09:59    40\n",
       "2000-01-01 00:14:59    11\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "447ee14a-38dc-4f3c-89cc-eb8fedc29fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.permutation(np.arange(len(dates))), index=dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "ce3dd80a-d3a8-4bb5-8e9b-e1b6708463fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:05:00</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 00:10:00</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                     open  high  low  close\n",
       "2000-01-01 00:00:00     2     9    1      1\n",
       "2000-01-01 00:05:00     7    10    0      8\n",
       "2000-01-01 00:10:00     6    11    6     11"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').ohlc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "3524b0c4-721c-4ff0-829e-bf00feb457c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(np.random.standard_normal((2, 4)),\n",
    "                    index=pd.date_range(\"2000-01-01\", periods=2,freq=\"W-WED\"),\n",
    "                    columns=[\"Colorado\", \"Texas\", \"New York\", \"Ohio\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "00f6d5ac-3703-4e4c-9fef-9e80e9de56c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
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      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-12 -0.962377 -0.450726 -0.350091  0.680579"
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     "execution_count": 258,
     "metadata": {},
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   "source": [
    "frame"
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  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "aa5d5373-344d-4369-9518-6c8f6464f618",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_daily = frame.resample('D').asfreq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "04f5f5f7-26c9-4190-b8c5-774b62b9cf59",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
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       "      <th>2000-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.962377</td>\n",
       "      <td>-0.450726</td>\n",
       "      <td>-0.350091</td>\n",
       "      <td>0.680579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-06       NaN       NaN       NaN       NaN\n",
       "2000-01-07       NaN       NaN       NaN       NaN\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -0.962377 -0.450726 -0.350091  0.680579"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "b47451b0-78a5-4ba0-a23f-a230459dd07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Colorado</th>\n",
       "      <th>Texas</th>\n",
       "      <th>New York</th>\n",
       "      <th>Ohio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
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       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>-0.663708</td>\n",
       "      <td>-0.890369</td>\n",
       "      <td>-0.084250</td>\n",
       "      <td>-1.128460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.962377</td>\n",
       "      <td>-0.450726</td>\n",
       "      <td>-0.350091</td>\n",
       "      <td>0.680579</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-06 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-07 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-08 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-09 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-10 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-11 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-12 -0.962377 -0.450726 -0.350091  0.680579"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.resample('D').ffill()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "66c800b1-5eef-4258-9226-a30a118ecf9b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.962377</td>\n",
       "      <td>-0.450726</td>\n",
       "      <td>-0.350091</td>\n",
       "      <td>0.680579</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-05 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-06 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-07 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-08       NaN       NaN       NaN       NaN\n",
       "2000-01-09       NaN       NaN       NaN       NaN\n",
       "2000-01-10       NaN       NaN       NaN       NaN\n",
       "2000-01-11       NaN       NaN       NaN       NaN\n",
       "2000-01-12 -0.962377 -0.450726 -0.350091  0.680579"
      ]
     },
     "execution_count": 262,
     "metadata": {},
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    "frame.resample('D').ffill(limit=2)"
   ]
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   "cell_type": "code",
   "execution_count": 263,
   "id": "fbcb8093-78bf-41fe-a9a9-5ccb4b1500c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            Colorado     Texas  New York      Ohio\n",
       "2000-01-06 -0.663708 -0.890369 -0.084250 -1.128460\n",
       "2000-01-13 -0.962377 -0.450726 -0.350091  0.680579"
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    "frame.resample('W-THU').ffill()"
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   "cell_type": "code",
   "execution_count": 265,
   "id": "2e9c5fd8-557b-451d-bbe2-403266e5404d",
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.DataFrame(np.random.standard_normal((24, 4)),\n",
    "                     index=pd.period_range(\"1-2000\", \"12-2001\",freq=\"M\"),\n",
    "                     columns=[\"Colorado\", \"Texas\", \"New York\", \"Ohio\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "cee97f1e-5a2f-48ba-93d7-d1013f2660cc",
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       "      <td>0.011044</td>\n",
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       "      <td>-0.077719</td>\n",
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       "      <td>0.211790</td>\n",
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       "      <td>0.206742</td>\n",
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       "      <th>2000-05</th>\n",
       "      <td>-0.918633</td>\n",
       "      <td>1.863907</td>\n",
       "      <td>-1.260471</td>\n",
       "      <td>-0.032477</td>\n",
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       "         Colorado     Texas  New York      Ohio\n",
       "2000-01 -1.328697  0.323342 -0.077991  0.530477\n",
       "2000-02  0.177197 -0.953248  0.619954  0.011044\n",
       "2000-03 -0.077719  0.335977  0.211790 -0.872048\n",
       "2000-04  0.206742  1.330055 -0.798372  0.168968\n",
       "2000-05 -0.918633  1.863907 -1.260471 -0.032477"
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     "metadata": {},
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   "source": [
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   "cell_type": "code",
   "execution_count": 267,
   "id": "44f83d7d-0059-4e1a-a670-bb535c471d42",
   "metadata": {},
   "outputs": [],
   "source": [
    "annual_frame = frame.resample(\"A-DEC\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "96b9f33c-6402-443f-97cc-d23c9bd5ffdd",
   "metadata": {},
   "outputs": [
    {
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       "      Colorado     Texas  New York      Ohio\n",
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   "execution_count": 269,
   "id": "82c441c6-6ab8-4bbc-af7f-3f5420fc7e01",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q1 -0.169342 -0.045813 -0.255151 -0.046352\n",
       "2000Q2 -0.169342 -0.045813 -0.255151 -0.046352\n",
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       "2000Q4 -0.169342 -0.045813 -0.255151 -0.046352\n",
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       "2001Q4 -0.129248 -0.060519 -0.111383  0.051910"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "annual_frame.resample(\"Q-DEC\").ffill()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "0979e1bc-4e02-470a-9deb-2f68546b0b29",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001Q4</th>\n",
       "      <td>-0.129248</td>\n",
       "      <td>-0.060519</td>\n",
       "      <td>-0.111383</td>\n",
       "      <td>0.051910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4 -0.169342 -0.045813 -0.255151 -0.046352\n",
       "2001Q1       NaN       NaN       NaN       NaN\n",
       "2001Q2       NaN       NaN       NaN       NaN\n",
       "2001Q3       NaN       NaN       NaN       NaN\n",
       "2001Q4 -0.129248 -0.060519 -0.111383  0.051910"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample(\"Q-DEC\", convention=\"end\").asfreq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "051c751b-8624-45f8-bf13-7b49e069d330",
   "metadata": {},
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       "      <th>Colorado</th>\n",
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       "      <td>-0.129248</td>\n",
       "      <td>-0.060519</td>\n",
       "      <td>-0.111383</td>\n",
       "      <td>0.051910</td>\n",
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       "    <tr>\n",
       "      <th>2002Q1</th>\n",
       "      <td>-0.129248</td>\n",
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       "      <td>-0.111383</td>\n",
       "      <td>0.051910</td>\n",
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       "      <th>2002Q2</th>\n",
       "      <td>-0.129248</td>\n",
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       "      <td>-0.111383</td>\n",
       "      <td>0.051910</td>\n",
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       "    <tr>\n",
       "      <th>2002Q3</th>\n",
       "      <td>-0.129248</td>\n",
       "      <td>-0.060519</td>\n",
       "      <td>-0.111383</td>\n",
       "      <td>0.051910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Colorado     Texas  New York      Ohio\n",
       "2000Q4 -0.169342 -0.045813 -0.255151 -0.046352\n",
       "2001Q1 -0.169342 -0.045813 -0.255151 -0.046352\n",
       "2001Q2 -0.169342 -0.045813 -0.255151 -0.046352\n",
       "2001Q3 -0.169342 -0.045813 -0.255151 -0.046352\n",
       "2001Q4 -0.129248 -0.060519 -0.111383  0.051910\n",
       "2002Q1 -0.129248 -0.060519 -0.111383  0.051910\n",
       "2002Q2 -0.129248 -0.060519 -0.111383  0.051910\n",
       "2002Q3 -0.129248 -0.060519 -0.111383  0.051910"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_frame.resample(\"Q-MAR\").ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "80f5a94a-6df9-46cf-bd23-91cf6b483003",
   "metadata": {},
   "outputs": [],
   "source": [
    "N=15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "321335be-72c6-46b7-9485-ecd62ea6ed27",
   "metadata": {},
   "outputs": [],
   "source": [
    "times=pd.date_range(\"2017-05-20 00:00\", freq=\"1min\",periods=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "4a768779-1df7-4413-8434-85e8106dcb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({\"time\":times,'value':np.arange(N)})"
   ]
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   "cell_type": "code",
   "execution_count": 276,
   "id": "11b4e5ae-0083-479c-9566-0eb2336b01dc",
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       "      <td>2017-05-20 00:04:00</td>\n",
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       "      <th>6</th>\n",
       "      <td>2017-05-20 00:06:00</td>\n",
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       "      <th>7</th>\n",
       "      <td>2017-05-20 00:07:00</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-05-20 00:08:00</td>\n",
       "      <td>8</td>\n",
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       "      <th>9</th>\n",
       "      <td>2017-05-20 00:09:00</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-05-20 00:11:00</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-05-20 00:12:00</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-05-20 00:13:00</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-05-20 00:14:00</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  value\n",
       "0  2017-05-20 00:00:00      0\n",
       "1  2017-05-20 00:01:00      1\n",
       "2  2017-05-20 00:02:00      2\n",
       "3  2017-05-20 00:03:00      3\n",
       "4  2017-05-20 00:04:00      4\n",
       "5  2017-05-20 00:05:00      5\n",
       "6  2017-05-20 00:06:00      6\n",
       "7  2017-05-20 00:07:00      7\n",
       "8  2017-05-20 00:08:00      8\n",
       "9  2017-05-20 00:09:00      9\n",
       "10 2017-05-20 00:10:00     10\n",
       "11 2017-05-20 00:11:00     11\n",
       "12 2017-05-20 00:12:00     12\n",
       "13 2017-05-20 00:13:00     13\n",
       "14 2017-05-20 00:14:00     14"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "aab9212f-9e6e-4e3e-8c30-1b30c8d03e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                     value\n",
       "time                      \n",
       "2017-05-20 00:00:00      5\n",
       "2017-05-20 00:05:00      5\n",
       "2017-05-20 00:10:00      5"
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   "source": [
    "df.set_index('time').resample('5min').count()"
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   "cell_type": "code",
   "execution_count": 278,
   "id": "7dd452fe-825d-4e2d-96fd-3491b669016e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({\"time\": times.repeat(3),\n",
    "                    \"key\": np.tile([\"a\", \"b\", \"c\"], N),\n",
    "                    \"value\": np.arange(N * 3.)})"
   ]
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   "execution_count": 279,
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       "      <th>5</th>\n",
       "      <td>2017-05-20 00:01:00</td>\n",
       "      <td>c</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-05-20 00:02:00</td>\n",
       "      <td>a</td>\n",
       "      <td>6.0</td>\n",
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      ],
      "text/plain": [
       "                 time key  value\n",
       "0 2017-05-20 00:00:00   a    0.0\n",
       "1 2017-05-20 00:00:00   b    1.0\n",
       "2 2017-05-20 00:00:00   c    2.0\n",
       "3 2017-05-20 00:01:00   a    3.0\n",
       "4 2017-05-20 00:01:00   b    4.0\n",
       "5 2017-05-20 00:01:00   c    5.0\n",
       "6 2017-05-20 00:02:00   a    6.0"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "03eea3b8-c970-4a19-8e2f-79861bb2c136",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_key =pd.Grouper(freq='5min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "adad4140-d561-4ca5-8cc8-088b1f9d6bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "resampled = (df2.set_index(\"time\").groupby([\"key\", time_key]).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "4b127793-199a-4224-a73d-6e927a73e3a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-05-20 00:05:00</th>\n",
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       "      <th>2017-05-20 00:10:00</th>\n",
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       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>110.0</td>\n",
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       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>185.0</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">c</th>\n",
       "      <th>2017-05-20 00:00:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-05-20 00:05:00</th>\n",
       "      <td>115.0</td>\n",
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       "    <tr>\n",
       "      <th>2017-05-20 00:10:00</th>\n",
       "      <td>190.0</td>\n",
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       "                         value\n",
       "key time                      \n",
       "a   2017-05-20 00:00:00   30.0\n",
       "    2017-05-20 00:05:00  105.0\n",
       "    2017-05-20 00:10:00  180.0\n",
       "b   2017-05-20 00:00:00   35.0\n",
       "    2017-05-20 00:05:00  110.0\n",
       "    2017-05-20 00:10:00  185.0\n",
       "c   2017-05-20 00:00:00   40.0\n",
       "    2017-05-20 00:05:00  115.0\n",
       "    2017-05-20 00:10:00  190.0"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "deb449b7-0ecd-448c-94a0-cd02c47fb13d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>2017-05-20 00:00:00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c</td>\n",
       "      <td>2017-05-20 00:05:00</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c</td>\n",
       "      <td>2017-05-20 00:10:00</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key                time  value\n",
       "0   a 2017-05-20 00:00:00   30.0\n",
       "1   a 2017-05-20 00:05:00  105.0\n",
       "2   a 2017-05-20 00:10:00  180.0\n",
       "3   b 2017-05-20 00:00:00   35.0\n",
       "4   b 2017-05-20 00:05:00  110.0\n",
       "5   b 2017-05-20 00:10:00  185.0\n",
       "6   c 2017-05-20 00:00:00   40.0\n",
       "7   c 2017-05-20 00:05:00  115.0\n",
       "8   c 2017-05-20 00:10:00  190.0"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "d9b3639e-4c04-4f2e-8a77-9c1c4fc54a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "close_px_all = pd.read_csv('examples/stock_px.csv',parse_dates=True,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "67b783be-6d3f-40b4-8480-078fa3fcd6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "close_px = close_px_all[[\"AAPL\", \"MSFT\", \"XOM\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "19006d3b-16c5-4402-86ec-09370fe07d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "close_px = close_px.resample(\"B\").ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "5d030952-2ace-41ea-87e6-ba4c3fa93178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_px[\"AAPL\"].plot()\n",
    "close_px[\"AAPL\"].rolling(250).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "3db53942-5997-46ab-84d4-56866b5f96f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003-01-09         NaN\n",
       "2003-01-10         NaN\n",
       "2003-01-13         NaN\n",
       "2003-01-14         NaN\n",
       "2003-01-15         NaN\n",
       "2003-01-16    0.009628\n",
       "2003-01-17    0.013818\n",
       "Freq: B, Name: AAPL, dtype: float64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std250 = close_px[\"AAPL\"].pct_change().rolling(250, min_periods=10).std()\n",
    "std250[5:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "53aca7bb-65ee-4b26-9798-58a1c4150446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std250.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "b0f458ff-7063-427a-b047-1fcd540fbf7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expanding_mean = std250.expanding().mean()\n",
    "close_px.rolling(60).mean().plot(logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "381d5814-b2fc-44a8-abdf-bf3b012b702c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>21.110000</td>\n",
       "      <td>29.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>7.425000</td>\n",
       "      <td>21.125000</td>\n",
       "      <td>29.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>7.433333</td>\n",
       "      <td>21.256667</td>\n",
       "      <td>29.473333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>7.432500</td>\n",
       "      <td>21.425000</td>\n",
       "      <td>29.342500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>7.402000</td>\n",
       "      <td>21.402000</td>\n",
       "      <td>29.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>389.351429</td>\n",
       "      <td>25.602143</td>\n",
       "      <td>72.527857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-11</th>\n",
       "      <td>388.505000</td>\n",
       "      <td>25.674286</td>\n",
       "      <td>72.835000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-12</th>\n",
       "      <td>388.531429</td>\n",
       "      <td>25.810000</td>\n",
       "      <td>73.400714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-13</th>\n",
       "      <td>388.826429</td>\n",
       "      <td>25.961429</td>\n",
       "      <td>73.905000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>391.038000</td>\n",
       "      <td>26.048667</td>\n",
       "      <td>74.185333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2292 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL       MSFT        XOM\n",
       "2003-01-02    7.400000  21.110000  29.220000\n",
       "2003-01-03    7.425000  21.125000  29.230000\n",
       "2003-01-06    7.433333  21.256667  29.473333\n",
       "2003-01-07    7.432500  21.425000  29.342500\n",
       "2003-01-08    7.402000  21.402000  29.240000\n",
       "...                ...        ...        ...\n",
       "2011-10-10  389.351429  25.602143  72.527857\n",
       "2011-10-11  388.505000  25.674286  72.835000\n",
       "2011-10-12  388.531429  25.810000  73.400714\n",
       "2011-10-13  388.826429  25.961429  73.905000\n",
       "2011-10-14  391.038000  26.048667  74.185333\n",
       "\n",
       "[2292 rows x 3 columns]"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px.rolling(\"20D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "b8867a7e-2a9f-497c-b4d4-0487df19ef21",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl_px = close_px[\"AAPL\"][\"2006\":\"2007\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "28724a66-9c8f-4281-95cc-046488cdead8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ma30 = aapl_px.rolling(30, min_periods=20).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "b8dc430c-5ffc-4c2d-bd7b-b9e5f891c9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ewma30 = aapl_px.ewm(span=30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "f3dd7dbf-aad6-485e-a39d-155986098375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x168d316f670>"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aapl_px.plot(style=\"k-\", label=\"Price\")\n",
    "ma30.plot(style=\"k--\", label=\"Simple Moving Avg\")\n",
    "ewma30.plot(style=\"k-\", label=\"EW MA\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "5fab87c5-dc54-46f4-807c-77fe6d00c9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_px = close_px_all[\"SPX\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "beb63d41-063d-457e-8628-94949e9ce6ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_rets = spx_px.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "e10180e9-c373-45b1-88f3-d9547a8eb828",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = close_px.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "a332c379-3ffa-4d6a-9d75-e24020ae6f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = returns[\"AAPL\"].rolling(125, min_periods=100).corr(spx_rets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "e65deddd-11e6-49a6-9e68-33953e0ee049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "1ecc6a27-f255-4f1c-8bc1-839a0df0bab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = returns.rolling(125, min_periods=100).corr(spx_rets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "81a02b88-edd1-4630-92cb-2b78e0d3e730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "98707df8-2735-4875-bdf9-3b6b066cf72b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import percentileofscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "350f4933-f61c-4a02-a620-2bcf5b94494b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_at_2percent(x):\n",
    "    return percentileofscore(x, 0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "2b77be12-4f60-423e-ab70-0ebd961ff51b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = returns[\"AAPL\"].rolling(250).apply(score_at_2percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "7b9c5d72-00c2-4248-a54b-5474c767360a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
